In [2]:
import pandas as pd
from textblob import TextBlob
from wordcloud import WordCloud
from collections import Counter
import json
In [3]:
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly import graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
In [4]:
df = pd.read_csv('Data/cleaned_data.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.head()
Out[4]:
In [5]:
df.info()
Date based Analysis¶
In [6]:
df['created_at'] = pd.to_datetime(df['created_at'])
In [7]:
df.head()
Out[7]:
In [8]:
import warnings
warnings.filterwarnings("ignore")
In [45]:
temp = df[['created_at']]
temp['created_at'] = temp['created_at'].dt.date
temp = temp['created_at'].value_counts().reset_index()
temp.sort_values(by='index', inplace=True)
temp.head()
Out[45]:
In [46]:
fig = plt.figure(figsize=(20,10))
ax = sns.barplot(x="index", y="created_at", hue="index", data=temp, dodge=False)
Top 5 Retweeted Tweet¶
In [11]:
df.dropna(subset=['clean_text'], inplace=True)
df.drop_duplicates('full_text', inplace=True)
In [12]:
fav = df[['retweet_count','full_text']].sort_values('retweet_count',ascending = False)[:5].reset_index()
for i in range(5):
print('{}). {} Counts\n==> {}\n'.format(i+1, fav['retweet_count'][i], fav['full_text'][i]))
Top 5 Favorite Tweets¶
In [13]:
fav = df[['favorite_count','full_text']].sort_values('favorite_count',ascending = False)[:5].reset_index()
for i in range(5):
print('{}). {} Counts\n==> {}\n'.format(i+1, fav['favorite_count'][i], fav['full_text'][i]))
In [14]:
def getSubjectivity(text):
return TextBlob(text).sentiment.subjectivity
def getPolarity(text):
return TextBlob(text).sentiment.polarity
In [15]:
def analyseSentiment(score):
if score < 0:
return 'Negative'
elif score ==0:
return 'Neutral'
else:
return 'Positive'
In [16]:
df['Subjectivity'] = df['clean_text'].apply(getSubjectivity)
df['Polarity'] = df['clean_text'].apply(getPolarity)
df['Sentiment'] = df['Polarity'].apply(analyseSentiment)
In [17]:
temp = df.groupby('Sentiment').count()['clean_text'].reset_index().sort_values(by='clean_text',ascending=False)
temp.style.background_gradient(cmap='Purples')
Out[17]:
In [18]:
fig = px.bar(temp,
x='Sentiment', y='clean_text',
title='Sentiment Analysis',
labels={'Sentiment':'Sentimetns', 'clean_text':'Tweets count'}
)
fig.show()
In [19]:
fig = go.Figure(go.Funnelarea(
text =temp.Sentiment,
values = temp.clean_text,
title = {"position": "top center", "text": "Funnel-Chart of Sentiment Distribution"}
))
fig.show()
In [20]:
plt.figure(1, figsize=(10,6))
plt.hist(df["created_at"],bins = 100);
plt.xlabel('Hours',size = 15)
plt.ylabel('No. of Tweets',size = 15)
plt.title('No. of Tweets per Hour',size = 15)
Out[20]:
In [21]:
plt.figure(figsize=(10,6))
sns.distplot(df['Polarity'], bins=30)
plt.title('Sentiment Distribution',size = 15)
plt.xlabel('Polarity',size = 15)
plt.ylabel('Frequency',size = 15)
plt.show();
Word Count¶
In [22]:
words = []
words = [word for i in df.clean_text for word in i.split()]
freq = Counter(words).most_common(30)
freq = pd.DataFrame(freq)
freq.columns = ['word', 'frequency']
freq.head()
Out[22]:
In [23]:
plt.figure(figsize = (15, 10))
sns.barplot(y="word", x="frequency",data=freq)
Out[23]:
In [24]:
neg_df = df[df['Sentiment'] == 'Negative']
neu_df = df[df['Sentiment'] == 'Neutral']
pos_df = df[df['Sentiment'] == 'Positive']
In [25]:
allWords = ''.join([twts for twts in df['clean_text']])
wordCloud = WordCloud(width=1000, height=700, background_color="white", random_state=21, max_font_size=119).generate(allWords)
wordCloud.to_file('All_Data.png')
plt.figure(num=None, figsize=(25, 8), dpi=180, edgecolor='k')
plt.imshow(wordCloud, interpolation="bilinear")
plt.axis('off')
plt.show()
In [26]:
allWords = ''.join([twts for twts in neg_df['clean_text']])
wordCloud = WordCloud(width=1000, height=700, background_color="white", random_state=21, max_font_size=119).generate(allWords)
wordCloud.to_file('ngwords_wordcloud.png')
plt.figure(num=None, figsize=(25, 8), dpi=180, edgecolor='k')
plt.imshow(wordCloud, interpolation="bilinear")
plt.axis('off')
plt.show()
In [27]:
allWords = ''.join([twts for twts in neu_df['clean_text']])
wordCloud = WordCloud(width=1000, height=700, background_color="white", random_state=21, max_font_size=119).generate(allWords)
wordCloud.to_file('neuwords_wordcloud.png')
plt.figure(num=None, figsize=(25, 8), dpi=180, edgecolor='k')
plt.imshow(wordCloud, interpolation="bilinear")
plt.axis('off')
plt.show()
In [28]:
allWords = ''.join([twts for twts in pos_df['clean_text']])
wordCloud = WordCloud(width=1000, height=700, background_color="white", random_state=21, max_font_size=119).generate(allWords)
wordCloud.to_file('poswords_wordcloud.png')
plt.figure(num=None, figsize=(25, 8), dpi=180, edgecolor='k')
plt.imshow(wordCloud, interpolation="bilinear")
plt.axis('off')
plt.show()
In [29]:
df_covid = pd.read_csv('Data/covid_cases.csv')
df_covid.drop('Unnamed: 0', axis=1, inplace=True)
In [30]:
start_date = df['created_at'][0]
end_date = df['created_at'][len(df)-1]
In [31]:
df_covid['Date'] = pd.to_datetime(df_covid['Date'])
In [32]:
mask = (df_covid['Date'] >= start_date) & (df_covid['Date'] <= end_date)
covid_cases = df_covid.loc[mask]
len(covid_cases)
Out[32]:
In [33]:
temp = covid_cases.groupby('Date').sum()
temp.reset_index(inplace=True)
In [34]:
fig = go.Figure(data=[
go.Bar(x=temp['Date'], y=temp['Confirmed'], name='Confirmed Cases'),
go.Bar(x=temp['Date'], y=temp['Recovered'], name='Recovered Cases'),
go.Bar(x=temp['Date'], y=temp['Deaths'], name='Deaths Cases')
])
# Change the bar mode
fig.update_layout(barmode='group')
fig.show()
In [35]:
temp = covid_cases.groupby('Country/Region').sum()
temp.reset_index(inplace=True)
top_confirmed_cases = temp.sort_values(by='Confirmed', ascending=False)
top_recovered_Cases = temp.sort_values(by='Recovered', ascending=False)
top_deaths_cases = temp.sort_values(by='Deaths', ascending=False)
In [36]:
fig = make_subplots(rows=1, cols=3,
shared_yaxes=True,
horizontal_spacing = 0.01,
subplot_titles=('Top 5 Countries in Cconfirmed',
'Top 5 Countries in Recovered',
'Top 5 Countries in Deaths'))
fig.add_trace(go.Bar(x=top_confirmed_cases['Country/Region'][:6],
y=top_confirmed_cases['Confirmed'][:6],
marker=dict(color=[4, 5, 6], coloraxis="coloraxis")),
1, 1)
fig.add_trace(go.Bar(x=top_recovered_Cases['Country/Region'][:6],
y=top_recovered_Cases['Recovered'][:6],
marker=dict(color=[2, 3, 5], coloraxis="coloraxis")),
1, 2)
fig.add_trace(go.Bar(x=top_deaths_cases['Country/Region'][:6],
y=top_deaths_cases['Deaths'][:6],
marker=dict(color=[2, 3, 5], coloraxis="coloraxis")),
1, 3)
fig.update_layout(coloraxis=dict(colorscale='Bluered_r'), showlegend=False)
fig.show()
Hashtags¶
In [37]:
hashtags = []
In [38]:
for i in df['hashtags']:
temp = i.replace('\'', "").strip('][').split(', ')
if '' not in temp:
hashtags.extend(temp)
In [39]:
temp = pd.DataFrame.from_dict({
'Hashtags':hashtags,
})
In [40]:
temp.head()
Out[40]:
In [41]:
temp = temp['Hashtags'].value_counts().reset_index()
fig = plt.figure(figsize=(20,10))
ax = sns.barplot(x="index", y="Hashtags", hue="index", data=temp[:10], dodge=False)
Saving Analyzed Data¶
In [42]:
df.head()
Out[42]:
In [43]:
covid_cases.head()
Out[43]:
In [44]:
df.to_csv('data/analysed_data.csv')
covid_cases.to_csv('data/covid_stats.csv')